Source code for abydos.distance._fager_mcgowan

# Copyright 2018-2020 by Christopher C. Little.
# This file is part of Abydos.
#
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"""abydos.distance._fager_mcgowan.

Fager & McGowan similarity
"""

from ._token_distance import _TokenDistance

__all__ = ['FagerMcGowan']


[docs]class FagerMcGowan(_TokenDistance): r"""Fager & McGowan similarity. For two sets X and Y, the Fager & McGowan similarity :cite:`Fager:1957,Fager:1963` is .. math:: sim_{FagerMcGowan}(X, Y) = \frac{|X \cap Y|}{\sqrt{|X|\cdot|Y|}} - \frac{1}{2\sqrt{max(|X|, |Y|)}} In :ref:`2x2 confusion table terms <confusion_table>`, where a+b+c+d=n, this is .. math:: sim_{FagerMcGowan} = \frac{a}{\sqrt{(a+b)(a+c)}} - \frac{1}{2\sqrt{max(a+b, a+c)}} .. versionadded:: 0.4.0 """ def __init__(self, tokenizer=None, intersection_type='crisp', **kwargs): """Initialize FagerMcGowan instance. Parameters ---------- tokenizer : _Tokenizer A tokenizer instance from the :py:mod:`abydos.tokenizer` package intersection_type : str Specifies the intersection type, and set type as a result: See :ref:`intersection_type <intersection_type>` description in :py:class:`_TokenDistance` for details. **kwargs Arbitrary keyword arguments Other Parameters ---------------- qval : int The length of each q-gram. Using this parameter and tokenizer=None will cause the instance to use the QGram tokenizer with this q value. metric : _Distance A string distance measure class for use in the ``soft`` and ``fuzzy`` variants. threshold : float A threshold value, similarities above which are counted as members of the intersection for the ``fuzzy`` variant. .. versionadded:: 0.4.0 """ super(FagerMcGowan, self).__init__( tokenizer=tokenizer, intersection_type=intersection_type, **kwargs )
[docs] def sim_score(self, src, tar): """Return the Fager & McGowan similarity of two strings. Parameters ---------- src : str Source string (or QGrams/Counter objects) for comparison tar : str Target string (or QGrams/Counter objects) for comparison Returns ------- float Fager & McGowan similarity Examples -------- >>> cmp = FagerMcGowan() >>> cmp.sim_score('cat', 'hat') 0.25 >>> cmp.sim_score('Niall', 'Neil') 0.16102422643817918 >>> cmp.sim_score('aluminum', 'Catalan') -0.048815536468908724 >>> cmp.sim_score('ATCG', 'TAGC') -0.22360679774997896 .. versionadded:: 0.4.0 """ if not src or not tar: return 0.0 self._tokenize(src, tar) a = self._intersection_card() apb = self._src_card() apc = self._tar_card() first = a / (apb * apc) ** 0.5 if a else 0.0 second = 1 / (2 * (max(apb, apc) ** 0.5)) return first - second
[docs] def sim(self, src, tar): r"""Return the normalized Fager & McGowan similarity of two strings. As this similarity ranges from :math:`(-\inf, 1.0)`, this normalization simply clamps the value to the range (0.0, 1.0). Parameters ---------- src : str Source string (or QGrams/Counter objects) for comparison tar : str Target string (or QGrams/Counter objects) for comparison Returns ------- float Normalized Fager & McGowan similarity Examples -------- >>> cmp = FagerMcGowan() >>> cmp.sim('cat', 'hat') 0.25 >>> cmp.sim('Niall', 'Neil') 0.16102422643817918 >>> cmp.sim('aluminum', 'Catalan') 0.0 >>> cmp.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ return max(0.0, self.sim_score(src, tar))
if __name__ == '__main__': import doctest doctest.testmod()